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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) IJoICT (International Journal on Information and Communication Technology) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Data Science and Its Applications BERNAS: Jurnal Pengabdian Kepada Masyarakat eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)

Mahendra Dwifebri Purbolaksono

Universitas Telkom

Author-ID : 2490260

Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN Rizki Syafaat Amardita; Adiwijaya Adiwijaya; Mahendra Dwifebri Purbolaksono
JURIKOM (Jurnal Riset Komputer) Vol 9, No 1 (2022): Februari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i1.3793

As the times move forward, technologies are following to develop with the times, especially internet technology. Nowadays, people can find out information about shopping center via the Internet. One of the best-known shopping centers in Bandung is Paris van Java resort lifestyle place. The person's consideration of going to the shopping center is based on the location and opinions of the person who once visited that shop. With a huge amount of opinion and an underlying amount of information that is of great value causes the information to become dangerous if the person misunderstood it. With all these problems, sentiment analysis one of the solution that can prevent the problems of these misunderstanding, in which sentiment analysis works to analyze each text to determine the level of positive or negative sentiment values. In this study, the sentiment analysis dataset goes first to the preprocessing stage, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature then classified using the K-Nearest Neighbor method, The K-Nearest Neighbor method was chosen because this method is one method that can classify text and data, besides K-Nearest neighbor method has a good classification accuracy where the classification process is easy and quite simple in its implementation. With a system that built using TF-IDF Unigram and Euclidean Distance, the best accuracy value is 88.29%.

Sentiment Analysis on Beauty Product Review Using Modified Balanced Random Forest Method and Chi-Square Antika Putri Permata Wardani; Adiwijaya Adiwijaya; Mahendra Dwifebri Purbolaksono
Journal of Information System Research (JOSH) Vol 4 No 1 (2022): October 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Internet users in Indonesia have used e-commerce services to buy various products. For example, one website that provides information services about women's beauty products is Female Daily. On the website, there are reviews of beauty products. The review feature is one feature that helps users in determining which beauty products to buy. Unfortunately, many reviews will take a long time to read, and it is almost impossible for users to read all the information. Therefore, research is needed to make it easier for users to consider products such as sentiment analysis. Sentiment analysis aims to classify opinions, namely, user reviews, into positive, neutral, and negative opinions. In this study, sentiment analysis uses the Modified Balanced Random Forest(MBRF) and Chi-square method as feature selection. The best model from this study produces an average accuracy and an average f1-score of 81.75% and 71.90%, respectively.

Telkom University Opinion Topic Modeling on Twitter Using Latent Dirichlet Allocation During Covid-19 Pandemic Tandya Rizky Pratama; Donni Richasdy; Mahendra Dwifebri Purbolaksono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4426

In the current digital era, the development of information technology is growing rapidly. The development of information technology is followed by the development of social media, one of the social media that is on the rise is Twitter. Because there are many Twitter users around the world, Twitter stores a lot of data that can be used for something, one of which is to determine the category of public opinion about a company or university, in this study the focus is more on the category of public opinion about Telkom University. The public opinion can be grouped or categorized to make it easier to determine the topic being discussed. Determining opinions manually will take a long time due to the large number of tweets. Therefore, there must be another method to determine the categories of public opinion on Twitter. One of them is the Latent Dirichlet Allocation (LDA) method with a dataset of tweets of Indonesian-language Twitter users. With this method, grouping tweets on a large scale is more efficient. From the modeling made, the most optimum results obtained with a coherence score using the c_umass method of -15.33029 with a combination of 9 topics, 0.31 alpha value, and 0.01 beta value.

Analysis of Telkom University News Subjects on Popular Indonesian News Portals Using a Combination of Hidden Markov Model (HMM) and Rule Based Methods Rendhy Al-Farrel; Donni Richasdy; Mahendra Dwifebri Purbolaksono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4566

News media are often found in everyday life as a means of information for the public about something that is happening. In news articles, it is common to see several sentences that support the object to increase its popularity by being promoted by the subject. Part of Speech Tagging can determine the class of words in the sentence according to Tagsets provided by the corpus. That way, the search for the subject in the news article can be found from the word class obtained from a corpus. This research was focused on finding the subject "who" repeatedly spreading the news about Telkom University by using Part of Speech Tagging with the Hidden Markov Model and Rule Based on a news dataset from popular news portals about Telkom University. The process is taking all news about Telkom University on popular news portals and classifying it using the Hidden Markov Model and Rule-Based. We conducted to enhance the research results by changing the probability estimator on Hidden Markov Model. After running some scenarios, the best results obtained by the Hidden Markov Model and Rule-Based are the Accuracy of 94.96%, the Precision of 94.99%, the Recall of 94.96%, and the F1-Score of 94.95%.

Sentiment Analysis of Telkom University as the Best BPU in Indonesia Using the Random Forest Method Irfan Budi Prakoso; Donni Richasdy; Mahendra Dwifebri Purbolaksono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4567

In this day and age, social media has become a necessity for every human being. By using social media networks, users can easily exchange information, especially on linkedin social media. Linkedin is a social media network that can search for information openly, mainly used for professional networking. It will be easier and more practical to connect with professionals worldwide. Like identity, LinkedIn is often used as a medium to introduce yourself or your business to potential colleagues or companies for various purposes. Social media networks are often used to deliver information in various institutions at State Universities (PTN) and Private Universities (PTS). For example, it conveys information about state and private universities' achievements (PTS) achievements. Telkom University uses Linkedin to convey the achievements that have been achieved. This triggers the public to see posts that are positive, negative, or neutral. This study aims to conduct a sentiment analysis about Telkom University which has become the best private university in Indonesia, based on opinions submitted on LinkedIn social media. The process carried out in this study is to process all opinion data about Telkom University, which is the best private university in Indonesia, from Linkedin and then classification using the Random Forest method based on the categories of positive, neutral, and negative sentiments. Sentiment analysis results that have been obtained using the Random Forest classification method are 92.85% accuracy, 83.33% precision, 91.67% recall, and 84.13% F1-score%.

POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News Winkie Setyono; Donni Richasdy; Mahendra Dwifebri Purbolaksono
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1983

News is a medium of daily information usually obtained by the public. The news consists of a lot of information in it and is composed of sentence structures. Each language is unique with its own sentence structure, like Indonesian and other foreign languages. But nowadays, many media mix Indonesian with foreign languages, making the sentence structure different from Bahasa Indonesia. To classify these words, Part Of Speech Tagging needed to determine the class of words composed of sentences by learning from the Corpus of each language. With the new sentence structure, POS Tagger requires a larger Corpus to learn. The language structure can determine the results of tagging from the POS Tagger. If there are words that are not in the Corpus, it can reduce the accuracy of the POS Tagger. We conducted to enhance the research results by adding data with a different sentence structure from the Indonesian Language Corpus using sentences from online media. Added about 242 sentences with 7,043 tokens on Corpus focused on Foreign Word tags, which total 3819 tags. After doing some testing and scenarios, the results of the accuracy of POS Tagger show an accuracy of 94.7% using the Hidden Markov Model method with the F1-Score tag FW 78%.

Sentiment Analysis on Indonesian Movie Review Using KNN Method With the Implementation of Chi-Square Feature Selection Imam Prayoga; Mahendra Dwifebri Purbolaksono; Adiwijaya Adiwijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5522

The advancement and development of the internet is used by the people to support various sectors, one of which is the film industry. Nowadays, people can easily access various movies from available sites. This convenience had led to many reviews about a movie that can be obtained easily. This movie review is very influential on the variety of movies. Freedom of expression on the internet, makes the reviews of a movie vary. For this reason, it is necessary to analyze the sentiment of he movie reviews that are positive or negative. In this research, a sentiment analysis model is build using chi-square selection feature with the KNN algorithm. The final result of this research is able to provide the best classification model with the implementation of stemming. The value of k = 267 in selectkbest at the feature selection stage using chi-square, and using the value of K = 11 in the KNN parameter. This model produces f1 score value of 86.98%.

Implementasi Mutual Information Dan Bayesian Network Untuk Klasifikasi Data Microarray Mahendra Dwifebri Purbolaksono; Adiwijaya Adiwijaya
eProceedings of Engineering Vol 4, No 2 (2017): Agustus, 2017
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract.Kanker merupakan salah satu penyebab utama kematian seseorang diseluruh dunia. Menurut data WHO, ada sekitar 14 juta kasus kanker baru di tahun 2012. Karena hal itu pengawasan sejak dini dibutuhkan guna mencegah pertumbuhan kanker. Selain itu pendeteksian secara dini juga merupakan hal yang dibutuhkan. Salah satu cara mendeteksi yaitu melalui ekspresi gen. Ekspresi gen merupakan metode ekstrasi gen menjadi data yang menjadi data bernama microarray. Data microarray memungkinkan terjadi proses pengklasifikasi secara langsung namun atribut dalam suatu record sangat besar sehingga memakan waktu komputasi yang lama. Karenanya dibutuhkan sistem yang dapat menyelesaikan masalah tersebut. Pada penelitian ini, sistem menggunakan pendekatan machine learning yaitu Bayesian Network. Sedangkan untuk seleksi fitur menggunakan Mutual information. Hal ini berguna untuk mengurangi attribute yang terlalu banyak. Untuk pengukuran menggunakan F1-score. Sistem yang dibangun mampu mengklasifikasi kanker dengan f1-score tertinggi mencapai 91.06%. Keyword: Bayesian Network, Mutual Information, Microarray.

Klasifikasi Teks Multi Label Pada Hadis Terjemahan Bahasa Indonesia Menggunakan Chi-square Dan Svm Fakhri Taufiqurrahman; Said Al Faraby; Mahendra Dwifebri Purbolaksono
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

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Hadis yaitu pedoman dalam islam setelah Al-Quran yang dijadikan sebagai sumber hukum dalam islam. Akan tetapi terdapat permasalahan ketika mentukan hadis mana saja yang merupakan anjuran, larangan, dan informasi. Oleh karena itu dibutuhkan klasifikasi teks untuk mengelompokan hadis ke dalam satu atau lebih dari anjuran, larangan, dan informasi, yang disebut dengan klasifikasi multi-label. Permasalahan dalam klasifikasi teks yaitu terdapat banyak fitur, sehingga perlu dilakukan seleksi fitur dengan tujuan memangkas fitur yang ada kemudian mentukan fitur paling berpengaruh terhadap kelas target. Pada penelitian ini Chi-Square digunakan untuk melakukan seleksi fitur dan Support Vector Machine (SVM) untuk melakukan klasifikasi teks. Dengan menggunakan metode evaluasi performa Macro F1-Score hasil yang didapat ketika menggunakan Chi-Square dan SVM yaitu sebesar 75.32%. Kata kunci : hadis, klasifikasi teks, multi-label, chi-square, support vector machine.

PEMODELAN SISTEM DAN DATASEBAGAI DASAR ANALISIS SPESIFIKASI KEBUTUHAN UNTUK PROSES CLOUD COMPUTING DI EFARMING CORPORA COMMUNITY BANDUNG Gede Agung Ary Wisudiawan; Yudi Priyadi; Mahendra Dwifebri Purbolaksono; Angelia Brigitta Maharani M.P; Pramoedya Syachrizalhaq Lyanda; M. Mujahid Biagi Usama; Abdurrahman Al Afifi; Tetuko Muhammad Hanurogo
Prosiding COSECANT : Community Service and Engagement Seminar Vol 1, No 2 (2021)
Publisher : Universitas telkom

Efarming Corpora adalah sebuah komunitas penggiat pertanian yang meliputi pertanian, peternakan, perikanan, perkebunan, kehutanan dan energi terbarukan. Komunitas ini belum memiliki manajemen keuangan dan penjualan hasil tani, sehingga ada beberapa persoalan yang dihadapi. Persoalan tersebut seperti pencatatan keuangan yang masih menggunakan banyak kertas dan kemungkinan terjadi human error sangat besar, dan penjualan hasil tani yang tidak terorganisir. Tantangan ini tentu menjadi sarana yang dapat digunakan oleh para dosen yang meneliti bidang software engineering terutama kelompok abdimas ini untuk memberikan solusi atas permasalahan yang ada. Adapun solusi yang ingin dikembangakan adalah gambaran teknis UML yang nantinya dapat digunakan untuk pembangunan sistem informasi keuangan dan penjualan hasil tani.

Co-Authors Abdurrahman Al Afifi Adiwijaya Adiwijaya Adiwijaya Adnan Imam Hidayat Al Faraby, Said Alex Wira Wilantapoera Angelia Brigitta Maharani M.P Anggitha Yohana Clara Antika Putri Permata Wardani Bima Mahardika Wirawan Deninsyah Tiya Bella Pratama Donni Richasdy Fahmi Hamzah Fahriza Ichsani Rafif Fakhri Taufiqurrahman Felia Novitasari Fhira Nhita Gde Agung Brahmana Suryanegara Gede Agung Ary Wisudiawan Gede Agung Ary Wisudiawan Ghinaa Zain Nabiilah Ihsan Ahsanu Amala Imam Prayoga Irfan Budi Prakoso Kemas Muslim Lhaksmana, Kemas Muslim M. Mujahid Biagi Usama Mohamad Rizki Nugraha Muhammad Farhan Muhammad Hadiyan Wicaksono Muhammad Irvan Tantowi Naufal Hilmiaji Novia Russelia Wassi Pramoedya Syachrizalhaq Lyanda Pramoedya Syachrizalhaq Lyanda Rendhy Al-Farrel Rendi Kustiawan Rizal Adi Putranto Rizki Syafaat Amardita Tandya Rizky Pratama Tetuko Muhammad Hanurogo Widi Astuti Winkie Setyono Yudi Priyadi

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